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All Journal International Journal of Advances in Intelligent Informatics Proceeding of Community Service and Engagement
Aulia Ayu Dyah Lestari
Telkom University
Computer Science & IT

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Implementasi Presensi Siswa Berbasis Machine Learning Di SMK Telekomunikasi Telesandi Bekasi Suci Aulia; Sugondo Hadiyoso; Ridha Muldina Negara; Aulia Ayu Dyah Lestari; Audry Stevany; Patricia Lovenia Garcia; Muhammad Alfachri Akbar
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 5 No. 2 (2025): Prosiding COSECANT : Community Service and Engagement Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v5i2.10334

Abstract

Sistem presensi manual berbasis kertas yang masih digunakan di SMK Telekomunikasi Telesandi Bekasi menimbulkan berbagai permasalahan seperti proses double entry yang memakan waktu, kerentanan terhadap kesalahan pencatatan, dan keterlambatan pelaporan data kehadiran. Penelitian ini bertujuan mengimplementasikan sistem presensi siswa berbasis Machine Learning menggunakan teknologi deteksi wajah dan deteksi senyuman untuk meningkatkan efisiensi administrasi sekolah. Metodologi penelitian menggunakan pendekatan partisipatif yang melibatkan pihak sekolah pada setiap tahapan, mulai dari diskusi awal, pengumpulan dan analisis data, pengembangan sistem berbasis Python dan OpenCV, pelatihan dan sosialisasi, hingga evaluasi dan monitoring implementasi. Sistem yang dikembangkan terdiri dari tiga komponen utama yaitu aplikasi presensi berbasis Python dan OpenCV, model deteksi wajah serta senyuman sebagai validator presensi, dan dashboard web berbasis cloud untuk pemantauan kehadiran. Hasil implementasi menunjukkan bahwa fitur pendeteksian wajah, pembuatan dataset, dan proses face recognition berjalan dengan baik meskipun modul penyimpanan otomatis ke cloud masih dalam tahap pengembangan. Evaluasi melalui post-test terhadap 142 responden menunjukkan mayoritas peserta memberikan respons positif dan mengalami peningkatan pemahaman konsep Machine Learning. Sistem ini berhasil meningkatkan efisiensi proses presensi, mengurangi ketergantungan pencatatan manual, serta meningkatkan literasi teknologi siswa dan guru dalam bidang Artificial Intelligence, Computer Vision, dan pemrograman Python yang selaras dengan pendidikan vokasi berbasis teknologi.
TUD-BISINDO: A new dataset and its recognition system using YOLO Muhammad Raihan; Aulia Ayu Dyah Lestari; Suci Aulia; Yuli Sun Hariyani; Devira Anggi Maharani
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2329

Abstract

This study addresses the urgent need for digital inclusivity by developing a high-precision, real-time recognition system for Bahasa Isyarat Indonesia (BISINDO). The main new idea in this study is the creation of the Telkom University Database (TUD)-BISINDO, which is a strong and varied collection of data designed to fix the problems of current sign language databases, like not having enough different environments and camera angles. The TUD-BISINDO was created using 1,040 original images and added 780 more images to fix problems like differences in lighting, angles, and hand features that were often found in earlier datasets. The YOLOv8l model, improved with the AdamW optimizer and a flexible learning rate, performed exceptionally well with a mAP50 of 99.30% mAP50-95 of 85.40%, 99.80% precision, and 99.70% recall. These results demonstrate that the model significantly outperforms the previous YOLOv5 baseline across all primary metrics. The model has outstanding precision in recognizing real-time finger movements. However, complicated gestures, including the G and Z letters, require additional improvement. This research enhances sign language recognition technology, encouraging inclusion and improving accessibility for real-time communication. Future studies should focus on diversifying the dataset and maximizing performance in challenging conditions.
Co-Authors Audry Stevany Devira Anggi Maharani Muhammad Alfachri Akbar Muhammad Raihan Patricia Lovenia Garcia Ridha Muldina Negara Suci Aulia Sugondo Hadiyoso YULI SUN HARIYANI